Efficiency and comparability of using new evidence platforms for updating recommendations: Experience with a type-2 diabetes guideline in Colombia.

Introduction: Updating recommendations for guidelines requires a comprehensive and efficient literature search. Although new information platforms are available for developing groups, their relative contributions to this purpose remain uncertain. Methods: As part of a review/update of eight selected evidence-based recommendationsfor type 2 diabetes, we evaluated the following five literature search approaches (targeting systematic reviews, using predetermined criteria): PubMed for MEDLINE, Epistemonikos database basic search, Epistemonikos database using a structured search strategy, Living overview of evidence (L.OVE) platform, and TRIP database. Three reviewers independently classified the retrieved references as definitely eligible, probably eligible, or not eligible. Those falling in the same "definitely" categories for all reviewers were labelled as "true" positives/negatives. The rest went to re-assessment and if found eligible/not eligible by consensus became "false" negatives/positives, respectively. We described the yield for each approach and computed "diagnostic accuracy" measures and agreement statistics. Results: Altogether, the five approaches identified 318 to 505 references for the eight recommendations, from which reviewers considered 4.2 to 9.4% eligible after the two rounds. While Pubmed outperformed the other approaches (diagnostic odds ratio 12.5 versus 2.6 to 5.3), no single search approach returned eligible references for all recommendations. Individually, searches found up to 40% of all eligible references (n = 71), and no combination of any three approaches could find over 80% of them. Kappa statistics for retrieval between searches were very poor (9 out of 10 paired comparisons did not surpass the chance-expected agreement). Conclusion: Among the information platforms assessed, PubMed appeared to be more efficient in updating this set of recommendations. However, the very poor agreement among search approaches in the reference yield demands that developing groups add information from several (probably more than three) sources for this purpose. Further research is needed to replicate our findings and enhance our understanding of how to efficiently update recommendations.

Introducción: La actualización de recomendaciones de las guías de práctica clínica requiere búsquedas bibliográficas exhaustivas y eficientes. Aunque están disponibles nuevas plataformas de información para grupos desarrolladores, su contribución a este propósito sigue siendo incierta. Métodos: Como parte de una revisión/actualización de 8 recomendaciones basadas en evidencia seleccionadas sobre diabetes tipo 2, evaluamos las siguientes cinco aproximaciones de búsqueda bibliográfica (dirigidas a revisiones sistemáticas, utilizando criterios predeterminados): PubMed para MEDLINE; Epistemonikos utilizando una búsqueda básica; Epistemonikos utilizando una estrategia de búsqueda estructurada; plataforma (L.OVE) y TRIP . Tres revisores clasificaron de forma independiente las referencias recuperadas como definitivamente o probablemente elegibles/no elegibles. Aquellas clasificadas en las mismas categorías "definitivas" para todos los revisores, se etiquetaron como "verdaderas" positivas/negativas. El resto se sometieron a una nueva evaluación y, si se consideraban por consenso elegibles/no elegibles, se convirtieron en "falsos" negativos/positivos, respectivamente. Describimos el rendimiento de cada aproximación, junto a sus medidas de "precisión diagnóstica" y las estadísticas de acuerdo. Resultados: En conjunto, las cinco aproximaciones identificaron 318-505 referencias para las 8 recomendaciones, de las cuales los revisores consideraron elegibles el 4,2 a 9,4% tras las dos rondas. Mientras que Pubmed superó a las otras aproximaciones (odds ratio de diagnóstico 12,5 versus 2,6 a 53), ninguna aproximación de búsqueda identificó por sí misma referencias elegibles para todas las recomendaciones. Individualmente, las búsquedas identificaron hasta el 40% de todas las referencias elegibles (n=71), y ninguna combinación de cualquiera de los tres enfoques pudo identificar más del 80% de ellas. Las estadísticas Kappa para la recuperación entre búsquedas fueron muy pobres (9 de cada 10 comparaciones pareadas no superaron el acuerdo esperado por azar). Conclusiones: Entre las plataformas de información evaluadas, Pubmed parece ser la más eficiente para actualizar este conjunto de recomendaciones. Sin embargo, la escasa concordancia en el rendimiento de las referencias exige que los grupos desarrolladores incorporen información de varias fuentes (probablemente más de tres) para este fin. Es necesario seguir investigando para replicar nuestros hallazgos y mejorar nuestra comprensión de cómo actualizar recomendaciones de forma eficiente.

Artificial INtelligence to Support Informed DEcision-making (INSIDE) for Improved Literature Analysis in Oncology.

BACKGROUND: Defining optimal therapeutic sequencing strategies in prostate cancer (PC) is challenging and may be assisted by artificial intelligence (AI)-based tools for an analysis of the medical literature. OBJECTIVE: To demonstrate that INSIDE PC can help clinicians query the literature on therapeutic sequencing in PC and to develop previously unestablished practices for evaluating the outputs of AI-based support platforms. DESIGN, SETTING, AND PARTICIPANTS: INSIDE PC was developed by customizing PubMed Bidirectional Encoder Representations from Transformers. Publications were ranked and aggregated for relevance using data visualization and analytics. Publications returned by INSIDE PC and PubMed were given normalized discounted cumulative gain (nDCG) scores by PC experts reflecting ranking and relevance. INTERVENTION: INSIDE PC for AI-based semantic literature analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: INSIDE PC was evaluated for relevance and accuracy for three test questions on the efficacy of therapeutic sequencing of systemic therapies in PC. RESULTS AND LIMITATIONS: In this initial evaluation, INSIDE PC outperformed PubMed for question 1 (novel hormonal therapy [NHT] followed by NHT) for the top five, ten, and 20 publications (nDCG score, +43, +33, and +30 percentage points [pps], respectively). For question 2 (NHT followed by poly [adenosine diphosphate ribose] polymerase inhibitors [PARPi]), INSIDE PC and PubMed performed similarly. For question 3 (NHT or PARPi followed by 177Lu-prostate-specific membrane antigen-617), INSIDE PC outperformed PubMed for the top five, ten, and 20 publications (+16, +4, and +5 pps, respectively). CONCLUSIONS: We applied INSIDE PC to develop standards for evaluating the performance of AI-based tools for literature extraction. INSIDE PC performed competitively with PubMed and can assist clinicians with therapeutic sequencing in PC. PATIENT SUMMARY: The medical literature is often very difficult for doctors and patients to search. In this report, we describe INSIDE PC-an artificial intelligence (AI) system created to help search articles published in medical journals and determine the best order of treatments for advanced prostate cancer in a much better time frame. We found that INSIDE PC works as well as another search tool, PubMed, a widely used resource for searching and retrieving articles published in medical journals. Our work with INSIDE PC shows new ways in which AI can be used to search published articles in medical journals and how these systems might be evaluated to support shared decision-making.

Harnessing PubMed User Query Logs for Post Hoc Explanations of Recommended Similar Articles.

Searching for a related article based on a reference article is an integral part of scientific research. PubMed, like many academic search engines, has a "similar articles" feature that recommends articles relevant to the current article viewed by a user. Explaining recommended items can be of great utility to users, particularly in the literature search process. With more than a million biomedical papers being published each year, explaining the recommended similar articles would facilitate researchers and clinicians in searching for related articles. Nonetheless, the majority of current literature recommendation systems lack explanations for their suggestions. We employ a post hoc approach to explaining recommendations by identifying relevant tokens in the titles of similar articles. Our major contribution is building PubCLogs by repurposing 5.6 million pairs of coclicked articles from PubMed's user query logs. Using our PubCLogs dataset, we train the Highlight Similar Article Title (HSAT), a transformer-based model designed to select the most relevant parts of the title of a similar article, based on the title and abstract of a seed article. HSAT demonstrates strong performance in our empirical evaluations, achieving an F1 score of 91.72 percent on the PubCLogs test set, considerably outperforming several baselines including BM25 (70.62), MPNet (67.11), MedCPT (62.22), GPT-3.5 (46.00), and GPT-4 (64.89). Additional evaluations on a separate, manually annotated test set further verifies HSAT's performance. Moreover, participants of our user study indicate a preference for HSAT, due to its superior balance between conciseness and comprehensiveness. Our study suggests that repurposing user query logs of academic search engines can be a promising way to train state-of-the-art models for explaining literature recommendation.

The art and science of research methodology, biostatistics, manuscript writing, and publication ethics: An invigorating training for improving research aptitudes in southern Indian biomedical institutions.

The basic concepts of research are learned through systematic literature searches which form the basis of a research statement and research topic. Then the research question, hypothesis, aim, and objectives, as well as the experimental design, are developed. Given the context provided, the primary focus is on the importance of adequately training postgraduates and young research investigators in research methodology and project development. It is evident that there is a lack of proper training in these areas, and the rapid expansion of colleges in India exacerbates this issue. To address this, research students must receive comprehensive instruction in scientific research methodology, experimental design, statistics, scientific writing, publishing, and research ethics. Our team has been conducting workshops and symposia for more than two decades to improve the current teaching methods in these areas. Most recently, we organized a series of national and international workshops and seminars in multiple states across India to fortify the core concepts of scientific research for students and faculty members. This report highlights the key aspects of these workshops and the positive outcomes experienced by participants.

SMAFIRA: a literature-based web tool to assist researchers with retrieval of 3R-relevant information.

The search for 3R-relevant information is a prerequisite for any planned experimental approach considering animal use. Such a literature search includes all methods to replace, reduce and refine (3Rs) animal testing with the aim of improving animal welfare, and requires an intensive screening of literature databases reflecting the current state of knowledge in experimental biomedicine. We developed SMAFIRA, a freely available online tool to facilitate the screening of PubMed/MEDLINE for possible alternatives to animal testing. SMAFIRA employs state-of-the-art language models from the field of deep learning, and provides relevant literature citations in a ranked order, classified according to the experimental model used. By using this classification, the search for alternative methods in the biomedical literature will become much more efficient. The tool is available at https://smafira.bf3r.de.

Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative Analysis.

BACKGROUND: Large language models (LLMs) have raised both interest and concern in the academic community. They offer the potential for automating literature search and synthesis for systematic reviews but raise concerns regarding their reliability, as the tendency to generate unsupported (hallucinated) content persist. OBJECTIVE: The aim of the study is to assess the performance of LLMs such as ChatGPT and Bard (subsequently rebranded Gemini) to produce references in the context of scientific writing. METHODS: The performance of ChatGPT and Bard in replicating the results of human-conducted systematic reviews was assessed. Using systematic reviews pertaining to shoulder rotator cuff pathology, these LLMs were tested by providing the same inclusion criteria and comparing the results with original systematic review references, serving as gold standards. The study used 3 key performance metrics: recall, precision, and F1-score, alongside the hallucination rate. Papers were considered "hallucinated" if any 2 of the following information were wrong: title, first author, or year of publication. RESULTS: In total, 11 systematic reviews across 4 fields yielded 33 prompts to LLMs (3 LLMs×11 reviews), with 471 references analyzed. Precision rates for GPT-3.5, GPT-4, and Bard were 9.4% (13/139), 13.4% (16/119), and 0% (0/104) respectively (P<.001). Recall rates were 11.9% (13/109) for GPT-3.5 and 13.7% (15/109) for GPT-4, with Bard failing to retrieve any relevant papers (P<.001). Hallucination rates stood at 39.6% (55/139) for GPT-3.5, 28.6% (34/119) for GPT-4, and 91.4% (95/104) for Bard (P<.001). Further analysis of nonhallucinated papers retrieved by GPT models revealed significant differences in identifying various criteria, such as randomized studies, participant criteria, and intervention criteria. The study also noted the geographical and open-access biases in the papers retrieved by the LLMs. CONCLUSIONS: Given their current performance, it is not recommended for LLMs to be deployed as the primary or exclusive tool for conducting systematic reviews. Any references generated by such models warrant thorough validation by researchers. The high occurrence of hallucinations in LLMs highlights the necessity for refining their training and functionality before confidently using them for rigorous academic purposes.

How to undertake a scoping review.

RATIONALE AND KEY POINTS: Scoping reviews have become a popular approach for exploring what literature has been published on a particular field of interest. They can enable nurses to gain an overview of the contemporary evidence base relating to a practice area, treatment or specific patient demographic, for example. This article provides a concise guide for nurses planning to undertake a scoping review, explaining the various steps involved. REFLECTIVE ACTIVITY: 'How to' articles can help to update your practice and ensure it remains evidence-based. Apply this article to your practice. Reflect on and write a short account of: • How this article might improve your practice when undertaking a scoping review.• How you could use this information to educate nursing students and colleagues on the appropriate techniques and evidence base required for scoping the literature.

A dataset for analyzing the climate change response of grain quality of 48 Japanese rice cultivars with contrasting levels of heat tolerance.

Climate change has a significant impact on rice grain appearance quality; in particular, high temperatures during the grain filling period increase the rate of chalky immature grains, reducing the marketability of rice. Heat-tolerant cultivars have been bred and released to reduce the rate of chalky grain and improve rice quality under high temperatures, but the ability of these cultivars to actually reduce chalky grain content has never been demonstrated due to the lack of integrated datasets. Here, we present a dataset collected through a systematic literature search from publicly available data sources, for the quantitative analysis of the impact of meteorological factors on grain appearance quality of various rice cultivars with contrasted heat tolerance levels. The dataset contains 1302 field observations of chalky grain rates (%) - a critical trait affecting grain appearance sensitive to temperature shocks - for 48 cultivars covering five different heat-tolerant ranks (HTRs) collected at 44 sites across Japan. The dataset also includes the values of key meteorological variables during the grain filling period, such as the cumulative mean air temperature above the threshold temperature (TaHD), mean solar radiation, and mean relative humidity over 20 days after heading, obtained from a gridded daily meteorological dataset with a 1-km resolution developed by the National Agriculture and Food Research Organization. The dataset covers major commercial rice cultivars cultivated in Japan in different environmental conditions. It is a useful resource for analyzing the climate change impact on crop quality and assess the effectiveness of genetic improvements in heat tolerance. Its value has been illustrated in the research article entitled "Effectiveness of heat tolerance rice cultivars in preserving grain appearance quality under high temperatures - A meta-analysis", where the dataset was used to develop a statistical model quantifying the effects of high temperature on grain quality as a function of cultivar heat tolerance.

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed.

BACKGROUND: Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed. METHODS: A total of 1017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A control set of 1398 time-series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated. RESULTS: Three search filters were created in MEDLINE: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), most appropriate when there are limited resources to screen studies; a sensitivity-and-precision-maximising filter with higher sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), providing a balance between expediency and comprehensiveness; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions. CONCLUSION: Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.

PubMed and beyond: biomedical literature search in the age of artificial intelligence.

Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.

Exploring the Clinical Applications of Lemongrass Essential Oil: A Scoping Review.

Lemongrass is a medicinal plant that produces essential oil with a variety of therapeutic properties. Although lemongrass essential oil (LGEO) is promising in clinical applications, the existing knowledge on the efficacy and safety of LGEO remains limited. This scoping review aimed to identify, summarize, and synthesize existing literature related to the clinical applications of LGEO to provide an overview of its potential therapeutic benefits for patients. Three databases (PubMed, Web of Science, Scopus) were used following the PRISMA-ScR guidelines to find articles published between 1 January 2013, and 1 November 2022. A total of 671 records were identified and 8 articles were included in this scoping review. The majority of patients received oromucosal and topical treatment. The results of the studies suggest that LGEO might be a useful tool in the treatment of periodontitis, gingivitis and oral malodour, with similar efficacy to chlorhexidine (anti-gingivitis effect) and doxycycline (periodontitis). Additionally, LGEO has the potential for treating pityriasis versicolor and preventing skin aging and may have anti-dandruff effects. These findings not only underscore the diverse clinical potential of LGEO but also emphasize its comparable efficacy to established treatments. Further research is imperative to comprehensively evaluate LGEO's effectiveness, safety, mechanisms of action, potential interactions with other medications, and its long-term tolerability across diverse populations.

Use of comparative research in the study of chemistry education: A systematic analysis of the literature.

Comparative research can help identify the similarities of and differences in different contexts, enabling us to recognize more possibilities and strategies of enhancing our understanding of different aspects of education. To review and analyse the current status of using comparative research designs in chemistry education research, a Boolean keyword search in Scopus and Web of Science has been performed to retrieve articles published from January 2016 to February 2023. In total 7682 entries have been retrieved, but less than 0.01 % of them have applied comparative research in addressing issues of chemistry education. Twelve of the retrieved articles have met the inclusion criteria for further analysis. Though comparative research has been found to be used by over 65 % of the analysed articles to study teaching and learning in chemistry education, its application in curriculum development and student development has been demonstrated by some analysed studies. In addition, 75 % of the analysed articles have declared being funded by local and/or national funding sources. This suggests that the importance of comparative research in chemistry education has been recognized at the national level in various countries. It is hoped that the opportunities brought about by comparative research designs as revealed in this article can enhance the varieties and possibilities in chemistry education research in the forthcoming future.

Overview and quality assessment of health economic evaluations for homeopathic therapy: an updated systematic review.

INTRODUCTION: Likewise other medical interventions, economic evaluations of homeopathy contribute to the evidence base of therapeutic concepts and are needed for socioeconomic decision-making. A 2013 review was updated and extended to gain a current overview. METHODS: A systematic literature search of the terms 'cost' and 'homeopathy' from January 2012 to July 2022 was performed in electronic databases. Two independent reviewers checked records, extracted data, and assessed study quality using the Consensus on Health Economic Criteria (CHEC) list. RESULTS: Six studies were added to 15 from the previous review. Synthesizing both health outcomes and costs showed homeopathic treatment being at least equally effective for less or similar costs than control in 14 of 21 studies. Three found improved outcomes at higher costs, two of which showed cost-effectiveness for homeopathy by incremental analysis. One found similar results and three similar outcomes at higher costs for homeopathy. CHEC values ranged between two and 16, with studies before 2009 having lower values (Mean ± SD: 6.7 ± 3.4) than newer studies (9.4 ± 4.3). CONCLUSION: Although results of the CHEC assessment show a positive chronological development, the favorable cost-effectiveness of homeopathic treatments seen in a small number of high-quality studies is undercut by too many examples of methodologically poor research.

To help make decisions about homeopathy in healthcare, it is important, as with other medical treatments, to look at whether this treatment is effective in relation to its costs; in other words, to see if it is cost-effective. The aim of the current work was to update the picture of scientific studies available on this topic until 2012. To this purpose, two different researchers screened electronic literature databases for studies between January 2012 and July 2022 which assessed both the costs and the effects of a homeopathic treatment. They did this according to strict rules to make sure that no important study was missed. They reviewed the search results, gathered information from the studies, and assessed the quality of the studies using a set of criteria. They detected six additional new studies to the 15 already known from the previous work. Overall, they found that in 14 out of 21 studies, homeopathic treatment was at least equally effective for less or similar costs. For the remaining seven studies, costs were equal or higher for homeopathy. Of these seven, two were shown to be advantageous for homeopathy: indeed, specific economic analyses demonstrated that the benefit of the homeopathic treatment compensated for the higher costs. For the remaining five studies, the higher or equal costs of homeopathic treatment were not compensated by a better effect. The quality of the studies varied, with older studies generally being of lower quality compared to newer ones. The authors concluded that although the quality of research on homeopathy's cost-effectiveness has improved over time, and some high-quality studies show that it can be a cost-effective option, there are still many poorly conducted studies which make it difficult to offer a definitive statement. In other words, while there is some evidence that homeopathy can be effective in relation to its costs, there are still many studies that are not very reliable, which means that interested parties need to be cautious about drawing conclusions.

Prompt Engineers or Librarians? An Exploration.

The article explores the role of "prompt engineers" as a professional title, extending beyond the field of generative AI for developers, comparing certain tasks to the role of librarians, such as conducting search queries. It is possible for librarians to work with AI models in conjunction with traditional literature databases with emphasizing the need to recognize the distinct nature of these information resources. We should take cautious consideration of the specific skills worth acquiring to improve work efficiency, as well as an understanding of the development trends in generative AI and library science.

Natural Language Processing for Literature Search in Vascular Surgery: A Pilot Study Testing an Artificial Intelligence Based Application.

Introduction: The use of natural language processing (NLP) for a literature search has been poorly investigated in vascular surgery so far. The aim of this pilot study was to test the applicability of an artificial intelligence (AI) based mobile application for literature searching in a topic related to vascular surgery. Technique: A focused scientific question was defined to evaluate the performance of the AI application for a literature search and compare the results with the ground truth provided via a traditional literature search performed by human experts. Using pre-defined keywords, the literature search was performed automatically by the AI application through different steps, including quality assessment based on evaluation of the information available and quality filters using indicators of level of evidence, selection of publications based on relevancy filters using NLP, summarisation, and visualisation of the publications via the mobile app. A traditional literature search performed by human experts required 10 hours to check 154 original articles, among which 26 (16.9%) were truly related to the question, 63 (40.9%) related to the field but not to the specific question, and 65 (42.2%) were unrelated. The AI based search was performed in less than one hour, and, compared with traditional search, the method identified 17 original articles (48.6%) truly related to the question (p < .010), 18 (51.4%) related to the field but not to the specific question (p = .26), and no unrelated publications (p < .001). Fifteen truly related articles (88.2%) were identified jointly by the two methods. No significant difference was observed regarding the median number of citations, year of publications, and impact factor of journals. Discussion: The AI based method enabled a targeted, focused, and time saving literature search, although the selection of publications was not completely exhaustive. These results suggest that such an AI driven application is a complementary tool to help researchers and clinicians for continuous education and dissemination of knowledge.

Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery.

Objective: The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field. Data sources: The MEDLINE database was searched on April 2023. Review methods: The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery. Results: Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients. Conclusion: While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice.

A mapping exercise using automated techniques to develop a search strategy to identify systematic review tools.

The Systematic Review Toolbox aims provide a web-based catalogue of tools that support various tasks within the systematic review and wider evidence synthesis process. Identifying publications surrounding specific systematic review tools is currently challenging, leading to a high screening burden for few eligible records. We aimed to develop a search strategy that could be regularly and automatically run to identify eligible records for the SR Toolbox, thus reducing time on task and burden for those involved. We undertook a mapping exercise to identify the PubMed IDs of papers indexed within the SR Toolbox. We then used the Yale MeSH Analyser and Visualisation of Similarities (VOS) Viewer text-mining software to identify the most commonly used MeSH terms and text words within the eligible records. These MeSH terms and text words were combined using Boolean Operators into a search strategy for Ovid MEDLINE. Prior to the mapping exercise and search strategy development, 81 software tools and 55 'Other' tools were included within the SR Toolbox. Since implementation of the search strategy, 146 tools have been added. There has been an increase in tools added to the toolbox since the search was developed and its corresponding auto-alert in MEDLINE was originally set up. Developing a search strategy based on a mapping exercise is an effective way of identifying new tools to support the systematic review process. Further research could be conducted to help prioritise records for screening to reduce reviewer burden further and to adapt the strategy for disciplines beyond healthcare.

Literature-based predictions of Mendelian disease therapies.

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

Current evidence, clinical applications, and future directions of transcranial magnetic stimulation as a treatment for ischemic stroke.

Transcranial magnetic stimulation (TMS) is a non-invasive brain neurostimulation technique that can be used as one of the adjunctive treatment techniques for neurological recovery after stroke. Animal studies have shown that TMS treatment of rats with middle cerebral artery occlusion (MCAO) model reduced cerebral infarct volume and improved neurological dysfunction in model rats. In addition, clinical case reports have also shown that TMS treatment has positive neuroprotective effects in stroke patients, improving a variety of post-stroke neurological deficits such as motor function, swallowing, cognitive function, speech function, central post-stroke pain, spasticity, and other post-stroke sequelae. However, even though numerous studies have shown a neuroprotective effect of TMS in stroke patients, its possible neuroprotective mechanism is not clear. Therefore, in this review, we describe the potential mechanisms of TMS to improve neurological function in terms of neurogenesis, angiogenesis, anti-inflammation, antioxidant, and anti-apoptosis, and provide insight into the current clinical application of TMS in multiple neurological dysfunctions in stroke. Finally, some of the current challenges faced by TMS are summarized and some suggestions for its future research directions are made.

Forensic neuropathology in the past decade: a scoping literature review.

While there has been notable research activity in the field of clinical neuropathology over the recent years, forensic approaches have been less frequent. This scoping literature review explored original research on forensic neuropathology over the past decade (January 1, 2010, until February 12, 2022) using the MEDLINE database. The aims were to (1) analyze the volume of research on the topic, (2) describe meta-level attributes and sample characteristics, and (3) summarize key research themes and methods. Of 5053 initial hits, 2864 fell within the target timeframe, and 122 were included in the review. Only 3-17 articles were published per year globally. Most articles originated from the Europe (39.3%) and Asia (36.1%) and were published in forensic journals (57.4%). A median sample included 57 subjects aged between 16 and 80 years. The most common research theme was traumatic intracranial injury (24.6%), followed by anatomy (12.3%) and substance abuse (11.5%). Key methods included immunotechniques (31.1%) and macroscopic observation (21.3%). Although a number of novel findings were reported, most were of preliminary nature and will require further validation. In order to reach breakthroughs and validate novel tools for routine use, more research input is urged from researchers across the world. It would be necessary to ensure appropriate sample sizes and make use of control groups.